Model Evaluator
Evaluate and compare ML model performance with rigorous testing methodologies
npx skills add majiayu000/claude-skill-registry --skill model-evaluator --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Model Evaluator The Model Evaluator skill helps you rigorously assess and compare machine learning model performance across multiple dimensions. It guides you through selecting appropriate metrics, designing evaluation protocols, avoiding common statistical pitfalls, and making data-driven decisions about model selection. Proper model evaluation goes beyond accuracy scores. This skill covers evaluation across the full spectrum: predictive performance, computational efficiency, robustness, fairness, calibration, and production readiness. It helps you answer not just "which model is best?" but "which model is best for my specific use case and constraints?" Whether you are comparing LLMs, classifiers, or custom models, this skill ensures your evaluation methodology is sound and your conclusions are reliable. ## Core Workflows ### Workflow 1: Design Evaluation Protocol 1. **Define** evaluation objectives: - Primary goal (accuracy, speed, cost, etc.) - Secondary constraints - Failure modes to test - Real-world conditions to simulate 2. **Select** appropriate metrics: | Task Type | Primary Metrics | Secondary Metrics | |-----------|-----------------|-------------------| | Classificatio
- Core Workflows
- Workflow 1: Design Evaluation Protocol
- Workflow 2: Execute Comparative Evaluation
- Workflow 3: LLM-Specific Evaluation
- Quick Reference
- Best Practices
- Advanced Techniques
- Multi-Dimensional Evaluation
- LLM-as-Judge Protocol
- A/B Testing Framework
- Calibration Analysis
- Common Pitfalls to Avoid
What does the Model Evaluator skill do?
Evaluate and compare ML model performance with rigorous testing methodologies
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill model-evaluator --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.